Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations15288
Missing cells90
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory120.0 B

Variable types

DateTime1
Numeric15

Alerts

avg_flow_rate is highly overall correlated with avg_water_level and 7 other fieldsHigh correlation
avg_water_level is highly overall correlated with avg_flow_rate and 7 other fieldsHigh correlation
avg_water_temperature is highly overall correlated with max_water_temperature and 1 other fieldsHigh correlation
max_flow_rate is highly overall correlated with avg_flow_rate and 7 other fieldsHigh correlation
max_water_level is highly overall correlated with avg_flow_rate and 7 other fieldsHigh correlation
max_water_temperature is highly overall correlated with avg_water_temperature and 1 other fieldsHigh correlation
min_flow_rate is highly overall correlated with avg_flow_rate and 7 other fieldsHigh correlation
min_water_level is highly overall correlated with avg_flow_rate and 7 other fieldsHigh correlation
min_water_temperature is highly overall correlated with avg_water_temperature and 1 other fieldsHigh correlation
suspended_sediment_erosion is highly overall correlated with avg_flow_rate and 8 other fieldsHigh correlation
suspended_sediment_transport is highly overall correlated with avg_flow_rate and 8 other fieldsHigh correlation
suspended_solid_load is highly overall correlated with avg_flow_rate and 8 other fieldsHigh correlation
suspended_solids_concentration is highly overall correlated with suspended_sediment_erosion and 2 other fieldsHigh correlation
date has unique valuesUnique
suspended_sediment_erosion has 1887 (12.3%) zerosZeros

Reproduction

Analysis started2024-09-14 08:15:50.854025
Analysis finished2024-09-14 08:16:34.969974
Duration44.12 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

date
Date

UNIQUE 

Distinct15288
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size119.6 KiB
Minimum1980-11-01 00:00:00
Maximum2024-08-31 00:00:00
2024-09-14T10:16:35.486815image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:35.700107image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

avg_water_temperature
Real number (ℝ)

HIGH CORRELATION 

Distinct216
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9120552
Minimum0.2
Maximum21.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2024-09-14T10:16:35.997605image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile3.2
Q15.5
median9.6
Q314.1
95-th percentile17.6
Maximum21.8
Range21.6
Interquartile range (IQR)8.6

Descriptive statistics

Standard deviation4.8306741
Coefficient of variation (CV)0.48735343
Kurtosis-1.1573734
Mean9.9120552
Median Absolute Deviation (MAD)4.3
Skewness0.17908716
Sum151535.5
Variance23.335412
MonotonicityNot monotonic
2024-09-14T10:16:36.294032image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.2 221
 
1.4%
5 193
 
1.3%
4 173
 
1.1%
4.8 168
 
1.1%
5.2 168
 
1.1%
4.4 166
 
1.1%
5.8 161
 
1.1%
4.6 160
 
1.0%
3.8 153
 
1.0%
6.2 149
 
1.0%
Other values (206) 13576
88.8%
ValueCountFrequency (%)
0.2 1
 
< 0.1%
0.3 3
 
< 0.1%
0.4 2
 
< 0.1%
0.6 1
 
< 0.1%
0.7 1
 
< 0.1%
0.8 6
< 0.1%
0.9 6
< 0.1%
1 10
0.1%
1.1 9
0.1%
1.2 11
0.1%
ValueCountFrequency (%)
21.8 1
 
< 0.1%
21.7 3
< 0.1%
21.6 1
 
< 0.1%
21.5 3
< 0.1%
21.4 3
< 0.1%
21.3 1
 
< 0.1%
21.2 6
< 0.1%
21.1 5
< 0.1%
21 4
< 0.1%
20.9 3
< 0.1%

max_water_temperature
Real number (ℝ)

HIGH CORRELATION 

Distinct220
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.198947
Minimum0.5
Maximum22.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2024-09-14T10:16:36.585490image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile3.3
Q15.7
median9.9
Q314.3
95-th percentile18.2
Maximum22.4
Range21.9
Interquartile range (IQR)8.6

Descriptive statistics

Standard deviation4.955
Coefficient of variation (CV)0.48583448
Kurtosis-1.1250851
Mean10.198947
Median Absolute Deviation (MAD)4.3
Skewness0.19928398
Sum155921.5
Variance24.552025
MonotonicityNot monotonic
2024-09-14T10:16:37.257726image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.2 209
 
1.4%
4.8 192
 
1.3%
5 191
 
1.2%
5.2 180
 
1.2%
4.4 165
 
1.1%
5.8 164
 
1.1%
6 164
 
1.1%
6.2 159
 
1.0%
4.6 159
 
1.0%
4 155
 
1.0%
Other values (210) 13550
88.6%
ValueCountFrequency (%)
0.5 3
 
< 0.1%
0.6 1
 
< 0.1%
0.7 1
 
< 0.1%
0.8 4
 
< 0.1%
0.9 1
 
< 0.1%
1 8
0.1%
1.1 6
< 0.1%
1.2 11
0.1%
1.3 4
 
< 0.1%
1.4 13
0.1%
ValueCountFrequency (%)
22.4 1
 
< 0.1%
22.3 2
 
< 0.1%
22.2 2
 
< 0.1%
22.1 2
 
< 0.1%
22 2
 
< 0.1%
21.9 2
 
< 0.1%
21.8 3
< 0.1%
21.7 4
< 0.1%
21.6 4
< 0.1%
21.5 6
< 0.1%

min_water_temperature
Real number (ℝ)

HIGH CORRELATION 

Distinct213
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6312794
Minimum-0.1
Maximum21.4
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size119.6 KiB
2024-09-14T10:16:37.460457image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-0.1
5-th percentile3
Q15.3
median9.3
Q313.8
95-th percentile17.1
Maximum21.4
Range21.5
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation4.7329891
Coefficient of variation (CV)0.49141852
Kurtosis-1.1660593
Mean9.6312794
Median Absolute Deviation (MAD)4.2
Skewness0.17200289
Sum147243
Variance22.401185
MonotonicityNot monotonic
2024-09-14T10:16:37.719742image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.2 230
 
1.5%
4 190
 
1.2%
5 190
 
1.2%
4.8 177
 
1.2%
3.8 171
 
1.1%
14.8 166
 
1.1%
4.4 164
 
1.1%
5.2 160
 
1.0%
5.6 157
 
1.0%
6 155
 
1.0%
Other values (203) 13528
88.5%
ValueCountFrequency (%)
-0.1 2
 
< 0.1%
0.1 2
 
< 0.1%
0.2 3
< 0.1%
0.3 1
 
< 0.1%
0.4 2
 
< 0.1%
0.5 4
< 0.1%
0.6 5
< 0.1%
0.7 3
< 0.1%
0.8 6
< 0.1%
0.9 6
< 0.1%
ValueCountFrequency (%)
21.4 3
 
< 0.1%
21.2 6
< 0.1%
21.1 1
 
< 0.1%
21 4
< 0.1%
20.9 2
 
< 0.1%
20.8 2
 
< 0.1%
20.6 5
< 0.1%
20.5 4
< 0.1%
20.4 8
0.1%
20.3 3
 
< 0.1%

avg_water_level
Real number (ℝ)

HIGH CORRELATION 

Distinct274
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.137232
Minimum19
Maximum495
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2024-09-14T10:16:37.909549image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile42
Q159
median78
Q399
95-th percentile148
Maximum495
Range476
Interquartile range (IQR)40

Descriptive statistics

Standard deviation35.951824
Coefficient of variation (CV)0.42729982
Kurtosis9.6487635
Mean84.137232
Median Absolute Deviation (MAD)20
Skewness2.0809083
Sum1286290
Variance1292.5337
MonotonicityNot monotonic
2024-09-14T10:16:38.096363image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 233
 
1.5%
59 233
 
1.5%
72 228
 
1.5%
74 224
 
1.5%
55 220
 
1.4%
58 220
 
1.4%
54 220
 
1.4%
79 217
 
1.4%
77 216
 
1.4%
61 216
 
1.4%
Other values (264) 13061
85.4%
ValueCountFrequency (%)
19 1
 
< 0.1%
22 1
 
< 0.1%
24 1
 
< 0.1%
25 1
 
< 0.1%
27 9
0.1%
28 7
< 0.1%
29 11
0.1%
30 12
0.1%
31 7
< 0.1%
32 15
0.1%
ValueCountFrequency (%)
495 1
< 0.1%
450 1
< 0.1%
442 1
< 0.1%
432 1
< 0.1%
430 1
< 0.1%
414 1
< 0.1%
389 1
< 0.1%
387 2
< 0.1%
376 1
< 0.1%
350 1
< 0.1%

max_water_level
Real number (ℝ)

HIGH CORRELATION 

Distinct293
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.129579
Minimum25
Maximum542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2024-09-14T10:16:38.288508image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile48
Q168
median86
Q3108
95-th percentile165
Maximum542
Range517
Interquartile range (IQR)40

Descriptive statistics

Standard deviation39.541371
Coefficient of variation (CV)0.42458445
Kurtosis9.9670938
Mean93.129579
Median Absolute Deviation (MAD)20
Skewness2.1978506
Sum1423765
Variance1563.52
MonotonicityNot monotonic
2024-09-14T10:16:38.517645image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 248
 
1.6%
82 243
 
1.6%
84 234
 
1.5%
85 230
 
1.5%
88 225
 
1.5%
70 224
 
1.5%
90 218
 
1.4%
87 214
 
1.4%
74 213
 
1.4%
78 212
 
1.4%
Other values (283) 13027
85.2%
ValueCountFrequency (%)
25 1
 
< 0.1%
28 6
 
< 0.1%
29 3
 
< 0.1%
30 4
 
< 0.1%
31 9
 
0.1%
32 7
 
< 0.1%
33 17
0.1%
34 18
0.1%
35 26
0.2%
36 39
0.3%
ValueCountFrequency (%)
542 1
< 0.1%
491 1
< 0.1%
469 1
< 0.1%
467 1
< 0.1%
453 1
< 0.1%
439 1
< 0.1%
436 1
< 0.1%
424 1
< 0.1%
414 1
< 0.1%
395 1
< 0.1%

min_water_level
Real number (ℝ)

HIGH CORRELATION 

Distinct249
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.599359
Minimum11
Maximum451
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2024-09-14T10:16:38.720877image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile37
Q152
median70
Q391
95-th percentile135
Maximum451
Range440
Interquartile range (IQR)39

Descriptive statistics

Standard deviation33.177899
Coefficient of variation (CV)0.43886483
Kurtosis9.1020026
Mean75.599359
Median Absolute Deviation (MAD)19
Skewness1.9709382
Sum1155763
Variance1100.773
MonotonicityNot monotonic
2024-09-14T10:16:38.891081image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 272
 
1.8%
48 266
 
1.7%
47 265
 
1.7%
46 264
 
1.7%
53 248
 
1.6%
49 247
 
1.6%
51 247
 
1.6%
56 246
 
1.6%
54 240
 
1.6%
50 236
 
1.5%
Other values (239) 12757
83.4%
ValueCountFrequency (%)
11 1
 
< 0.1%
13 1
 
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
17 1
 
< 0.1%
19 3
< 0.1%
20 3
< 0.1%
21 1
 
< 0.1%
22 3
< 0.1%
23 7
< 0.1%
ValueCountFrequency (%)
451 1
< 0.1%
434 1
< 0.1%
425 1
< 0.1%
413 1
< 0.1%
389 1
< 0.1%
382 1
< 0.1%
358 1
< 0.1%
334 1
< 0.1%
326 1
< 0.1%
317 1
< 0.1%

suspended_solids_concentration
Real number (ℝ)

HIGH CORRELATION 

Distinct4082
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.659735
Minimum1.05
Maximum2000.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2024-09-14T10:16:39.064095image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1.05
5-th percentile2.56
Q16.66
median10.82
Q317.75
95-th percentile55.1255
Maximum2000.97
Range1999.92
Interquartile range (IQR)11.09

Descriptive statistics

Standard deviation74.59221
Coefficient of variation (CV)3.4438191
Kurtosis446.58769
Mean21.659735
Median Absolute Deviation (MAD)4.98
Skewness18.621017
Sum331134.03
Variance5563.9979
MonotonicityNot monotonic
2024-09-14T10:16:39.254142image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 44
 
0.3%
4.44 25
 
0.2%
12 22
 
0.1%
7 22
 
0.1%
9.96 21
 
0.1%
8.43 20
 
0.1%
7.05 20
 
0.1%
8 19
 
0.1%
7.62 18
 
0.1%
6.37 18
 
0.1%
Other values (4072) 15059
98.5%
ValueCountFrequency (%)
1.05 1
 
< 0.1%
1.06 2
 
< 0.1%
1.07 3
 
< 0.1%
1.08 4
 
< 0.1%
1.09 5
< 0.1%
1.1 5
< 0.1%
1.11 7
< 0.1%
1.12 10
0.1%
1.13 12
0.1%
1.14 12
0.1%
ValueCountFrequency (%)
2000.97 1
< 0.1%
2000.86 1
< 0.1%
2000.84 1
< 0.1%
2000.78 1
< 0.1%
2000.71 2
< 0.1%
2000.53 1
< 0.1%
2000.51 1
< 0.1%
2000.49 1
< 0.1%
2000.45 1
< 0.1%
2000.42 1
< 0.1%

suspended_solid_load
Real number (ℝ)

HIGH CORRELATION 

Distinct9686
Distinct (%)63.5%
Missing30
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean231.56506
Minimum1.49
Maximum50194.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2024-09-14T10:16:39.447821image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1.49
5-th percentile7.4085
Q121.4525
median43.35
Q398.615
95-th percentile627.2245
Maximum50194.83
Range50193.34
Interquartile range (IQR)77.1625

Descriptive statistics

Standard deviation1305.0911
Coefficient of variation (CV)5.6359587
Kurtosis558.19931
Mean231.56506
Median Absolute Deviation (MAD)27.7
Skewness19.629288
Sum3533219.7
Variance1703262.8
MonotonicityNot monotonic
2024-09-14T10:16:39.697303image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.19 9
 
0.1%
18.53 8
 
0.1%
15.98 8
 
0.1%
13.67 7
 
< 0.1%
9.44 7
 
< 0.1%
12.07 7
 
< 0.1%
28.72 7
 
< 0.1%
27.33 7
 
< 0.1%
18.38 7
 
< 0.1%
32.38 7
 
< 0.1%
Other values (9676) 15184
99.3%
(Missing) 30
 
0.2%
ValueCountFrequency (%)
1.49 1
< 0.1%
1.59 1
< 0.1%
1.73 1
< 0.1%
1.76 1
< 0.1%
1.87 1
< 0.1%
1.89 1
< 0.1%
1.97 2
< 0.1%
2 1
< 0.1%
2.04 1
< 0.1%
2.14 1
< 0.1%
ValueCountFrequency (%)
50194.83 1
< 0.1%
49272.42 1
< 0.1%
48842.49 1
< 0.1%
37524.15 1
< 0.1%
34556.33 1
< 0.1%
29134.88 1
< 0.1%
24136.73 1
< 0.1%
23866.37 1
< 0.1%
23374.05 1
< 0.1%
23291.65 1
< 0.1%

suspended_sediment_erosion
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct255
Distinct (%)1.7%
Missing30
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.082023201
Minimum0
Maximum17.68
Zeros1887
Zeros (%)12.3%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2024-09-14T10:16:39.894444image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.02
Q30.04
95-th percentile0.22
Maximum17.68
Range17.68
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.45983058
Coefficient of variation (CV)5.6061039
Kurtosis558.04382
Mean0.082023201
Median Absolute Deviation (MAD)0.01
Skewness19.626501
Sum1251.51
Variance0.21144417
MonotonicityNot monotonic
2024-09-14T10:16:40.073226image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 5421
35.5%
0.02 2717
17.8%
0 1887
 
12.3%
0.03 1383
 
9.0%
0.04 805
 
5.3%
0.05 531
 
3.5%
0.06 334
 
2.2%
0.07 270
 
1.8%
0.08 195
 
1.3%
0.09 159
 
1.0%
Other values (245) 1556
 
10.2%
ValueCountFrequency (%)
0 1887
 
12.3%
0.01 5421
35.5%
0.02 2717
17.8%
0.03 1383
 
9.0%
0.04 805
 
5.3%
0.05 531
 
3.5%
0.06 334
 
2.2%
0.07 270
 
1.8%
0.08 195
 
1.3%
0.09 159
 
1.0%
ValueCountFrequency (%)
17.68 1
< 0.1%
17.36 1
< 0.1%
17.21 1
< 0.1%
13.22 1
< 0.1%
12.18 1
< 0.1%
10.27 1
< 0.1%
8.5 1
< 0.1%
8.41 1
< 0.1%
8.24 1
< 0.1%
8.21 1
< 0.1%

suspended_sediment_transport
Real number (ℝ)

HIGH CORRELATION 

Distinct1311
Distinct (%)8.6%
Missing30
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean2.6806816
Minimum0.02
Maximum580.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2024-09-14T10:16:40.258249image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.09
Q10.25
median0.5
Q31.14
95-th percentile7.2615
Maximum580.96
Range580.94
Interquartile range (IQR)0.89

Descriptive statistics

Standard deviation15.105184
Coefficient of variation (CV)5.6348297
Kurtosis558.20567
Mean2.6806816
Median Absolute Deviation (MAD)0.32
Skewness19.629411
Sum40901.84
Variance228.16659
MonotonicityNot monotonic
2024-09-14T10:16:40.433509image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.19 224
 
1.5%
0.14 222
 
1.5%
0.22 213
 
1.4%
0.17 204
 
1.3%
0.21 203
 
1.3%
0.28 200
 
1.3%
0.26 194
 
1.3%
0.2 190
 
1.2%
0.3 189
 
1.2%
0.23 189
 
1.2%
Other values (1301) 13230
86.5%
ValueCountFrequency (%)
0.02 10
 
0.1%
0.03 22
 
0.1%
0.04 85
0.6%
0.05 157
1.0%
0.06 166
1.1%
0.07 162
1.1%
0.08 147
1.0%
0.09 165
1.1%
0.1 161
1.1%
0.11 182
1.2%
ValueCountFrequency (%)
580.96 1
< 0.1%
570.28 1
< 0.1%
565.31 1
< 0.1%
434.31 1
< 0.1%
399.96 1
< 0.1%
337.21 1
< 0.1%
279.36 1
< 0.1%
276.23 1
< 0.1%
270.53 1
< 0.1%
269.58 1
< 0.1%

avg_flow_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct1124
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.588125
Minimum9.06
Maximum926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2024-09-14T10:16:40.625161image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum9.06
5-th percentile20.3
Q132.1
median49.9
Q376
95-th percentile148
Maximum926
Range916.94
Interquartile range (IQR)43.9

Descriptive statistics

Standard deviation49.507343
Coefficient of variation (CV)0.79100217
Kurtosis32.11535
Mean62.588125
Median Absolute Deviation (MAD)20.5
Skewness3.9606795
Sum956847.26
Variance2450.977
MonotonicityNot monotonic
2024-09-14T10:16:40.809395image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
104 50
 
0.3%
102 47
 
0.3%
101 47
 
0.3%
107 46
 
0.3%
105 45
 
0.3%
103 45
 
0.3%
109 42
 
0.3%
106 41
 
0.3%
26 41
 
0.3%
33.3 40
 
0.3%
Other values (1114) 14844
97.1%
ValueCountFrequency (%)
9.06 1
 
< 0.1%
10.5 1
 
< 0.1%
11.5 1
 
< 0.1%
11.7 1
 
< 0.1%
13 1
 
< 0.1%
13.3 1
 
< 0.1%
13.6 1
 
< 0.1%
13.7 1
 
< 0.1%
13.9 3
< 0.1%
14.1 1
 
< 0.1%
ValueCountFrequency (%)
926 1
< 0.1%
786 1
< 0.1%
767 1
< 0.1%
757 1
< 0.1%
747 1
< 0.1%
707 1
< 0.1%
648 1
< 0.1%
627 1
< 0.1%
591 1
< 0.1%
561 1
< 0.1%

max_flow_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct1160
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.736787
Minimum11.8
Maximum1050
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2024-09-14T10:16:40.982244image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum11.8
5-th percentile23.1
Q139.575
median58.7
Q387.6
95-th percentile176
Maximum1050
Range1038.2
Interquartile range (IQR)48.025

Descriptive statistics

Standard deviation58.349596
Coefficient of variation (CV)0.79132273
Kurtosis28.590848
Mean73.736787
Median Absolute Deviation (MAD)22.5
Skewness3.8499989
Sum1127288
Variance3404.6753
MonotonicityNot monotonic
2024-09-14T10:16:41.156109image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107 74
 
0.5%
101 67
 
0.4%
102 66
 
0.4%
103 62
 
0.4%
105 61
 
0.4%
106 59
 
0.4%
111 57
 
0.4%
113 52
 
0.3%
108 52
 
0.3%
110 51
 
0.3%
Other values (1150) 14687
96.1%
ValueCountFrequency (%)
11.8 1
 
< 0.1%
14.5 1
 
< 0.1%
14.7 2
 
< 0.1%
14.8 1
 
< 0.1%
14.9 1
 
< 0.1%
15 2
 
< 0.1%
15.1 5
< 0.1%
15.2 4
< 0.1%
15.3 4
< 0.1%
15.4 2
 
< 0.1%
ValueCountFrequency (%)
1050 1
< 0.1%
912 1
< 0.1%
830 1
< 0.1%
827 1
< 0.1%
814 1
< 0.1%
766 1
< 0.1%
758 1
< 0.1%
715 1
< 0.1%
701 1
< 0.1%
653 1
< 0.1%

min_flow_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct1108
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.483891
Minimum6.02
Maximum809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size119.6 KiB
2024-09-14T10:16:41.328764image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum6.02
5-th percentile17.7
Q125.8
median40.8
Q365
95-th percentile124
Maximum809
Range802.98
Interquartile range (IQR)39.2

Descriptive statistics

Standard deviation42.273196
Coefficient of variation (CV)0.80545089
Kurtosis36.622683
Mean52.483891
Median Absolute Deviation (MAD)17.2
Skewness4.1183298
Sum802373.72
Variance1787.0231
MonotonicityNot monotonic
2024-09-14T10:16:41.527228image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 65
 
0.4%
25.5 53
 
0.3%
23.6 53
 
0.3%
24.3 52
 
0.3%
25 52
 
0.3%
22.8 51
 
0.3%
22.1 48
 
0.3%
21.3 47
 
0.3%
19.1 47
 
0.3%
20.1 47
 
0.3%
Other values (1098) 14773
96.6%
ValueCountFrequency (%)
6.02 1
< 0.1%
6.49 1
< 0.1%
7.62 1
< 0.1%
7.66 1
< 0.1%
8.07 1
< 0.1%
8.65 1
< 0.1%
8.85 1
< 0.1%
8.9 1
< 0.1%
9.14 1
< 0.1%
9.15 1
< 0.1%
ValueCountFrequency (%)
809 1
< 0.1%
751 1
< 0.1%
733 1
< 0.1%
713 1
< 0.1%
649 1
< 0.1%
564 1
< 0.1%
521 1
< 0.1%
505 1
< 0.1%
477 2
< 0.1%
475 1
< 0.1%

year
Real number (ℝ)

Distinct44
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001.4394
Minimum1980
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.8 KiB
2024-09-14T10:16:41.712817image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1980
5-th percentile1982
Q11991
median2001
Q32012
95-th percentile2022
Maximum2024
Range44
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.349482
Coefficient of variation (CV)0.0061703002
Kurtosis-1.1478865
Mean2001.4394
Median Absolute Deviation (MAD)10
Skewness0.054458471
Sum30598006
Variance152.50971
MonotonicityIncreasing
2024-09-14T10:16:41.880796image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
2000 366
 
2.4%
1992 366
 
2.4%
2004 366
 
2.4%
1984 366
 
2.4%
2016 366
 
2.4%
1996 366
 
2.4%
1988 366
 
2.4%
2008 366
 
2.4%
2003 365
 
2.4%
2005 365
 
2.4%
Other values (34) 11630
76.1%
ValueCountFrequency (%)
1980 61
 
0.4%
1981 365
2.4%
1982 365
2.4%
1983 365
2.4%
1984 366
2.4%
1985 365
2.4%
1986 365
2.4%
1987 365
2.4%
1988 366
2.4%
1989 365
2.4%
ValueCountFrequency (%)
2024 244
1.6%
2023 365
2.4%
2022 166
1.1%
2020 360
2.4%
2019 365
2.4%
2018 365
2.4%
2017 361
2.4%
2016 366
2.4%
2015 365
2.4%
2014 365
2.4%

month
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5367609
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size59.8 KiB
2024-09-14T10:16:42.021794image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4623576
Coefficient of variation (CV)0.52967482
Kurtosis-1.2164894
Mean6.5367609
Median Absolute Deviation (MAD)3
Skewness-0.010125007
Sum99934
Variance11.98792
MonotonicityNot monotonic
2024-09-14T10:16:42.172537image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 1332
8.7%
8 1305
8.5%
3 1301
8.5%
1 1297
8.5%
11 1289
8.4%
7 1286
8.4%
5 1285
8.4%
10 1277
8.4%
4 1258
8.2%
6 1237
8.1%
Other values (2) 2421
15.8%
ValueCountFrequency (%)
1 1297
8.5%
2 1187
7.8%
3 1301
8.5%
4 1258
8.2%
5 1285
8.4%
6 1237
8.1%
7 1286
8.4%
8 1305
8.5%
9 1234
8.1%
10 1277
8.4%
ValueCountFrequency (%)
12 1332
8.7%
11 1289
8.4%
10 1277
8.4%
9 1234
8.1%
8 1305
8.5%
7 1286
8.4%
6 1237
8.1%
5 1285
8.4%
4 1258
8.2%
3 1301
8.5%

Interactions

2024-09-14T10:16:31.503951image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:52.113109image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:54.471202image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:57.178673image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:59.782205image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:02.711885image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:05.416292image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:07.775744image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:11.151992image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:13.655116image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:16.301455image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:19.483479image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:21.878829image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:24.655108image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:27.601781image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:31.667240image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:52.459566image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:54.609593image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:57.335701image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:00.092602image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:02.923760image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:05.571909image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:07.945488image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:11.324264image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:13.801680image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:16.464271image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:19.650459image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:22.021500image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:24.885570image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:27.789509image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:31.833092image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:52.608373image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:54.751984image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:57.471434image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:00.378135image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:03.356314image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:05.708086image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:08.133263image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:11.486390image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:13.950576image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:16.612476image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:19.806852image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:22.163792image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:25.103837image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:27.969869image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:31.992503image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:52.748382image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:54.892059image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:57.615440image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:00.595744image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:03.521386image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:05.846246image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:08.284302image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:11.630897image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:14.092202image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:16.763755image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:20.015650image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:22.292743image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:25.291681image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:28.129360image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:32.145049image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:52.885265image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:55.034982image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:57.763611image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:00.885895image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:03.675046image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:05.996197image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:08.455216image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:11.772210image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:14.240914image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:16.910756image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:20.175831image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:22.450492image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:25.528892image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:28.308811image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:32.315502image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:53.029932image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:55.213228image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:57.910357image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:01.088382image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:03.826225image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:06.131400image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2024-09-14T10:16:11.919809image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:14.649217image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2024-09-14T10:16:20.314374image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2024-09-14T10:16:25.740179image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:28.498190image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:32.482347image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:53.164861image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:55.392648image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:58.047974image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:01.249171image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:03.967009image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:06.282470image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:08.767532image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:12.079393image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:14.797382image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:17.784929image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:20.449946image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:22.738403image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:25.915426image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:28.770357image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:32.667702image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:53.319366image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:55.624892image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:58.202408image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:01.481922image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:04.138207image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:06.443846image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:08.935138image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:12.262327image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:14.979206image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:18.024111image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:20.608187image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:22.909170image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:26.124746image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:29.075972image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:32.836118image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:53.458758image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:55.826720image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:58.420478image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:01.627668image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:04.306936image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:06.630032image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:09.183800image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:12.437464image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:15.198418image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:18.184028image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:20.748750image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:23.055896image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:26.361603image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:29.352161image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:32.999543image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:53.611900image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:56.070046image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:58.649401image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:01.799017image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:04.465108image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:06.804665image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:09.366717image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:12.663220image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:15.376664image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:18.377407image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:20.903107image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:23.210511image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:26.576171image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:29.788640image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:33.172087image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:53.760428image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:56.299311image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:58.890851image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:01.943655image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:04.610637image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:06.972779image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:09.584726image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:12.813611image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:15.543593image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:18.626436image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:21.065894image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:23.361664image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:26.806237image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:30.258722image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:33.304692image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:53.897654image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:56.436236image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:59.053658image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:02.084970image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:04.748773image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:07.129040image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:09.758582image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:12.969818image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:15.681638image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:18.780065image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:21.201712image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:23.537098image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:26.949538image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:30.634208image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:33.446618image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:54.032880image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:56.575777image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:59.249719image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:02.234294image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:04.904566image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:07.292083image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:10.155837image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:13.127742image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:15.849734image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:18.940252image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:21.366413image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:23.775410image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:27.121327image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:30.867558image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:33.601688image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:54.183312image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:56.854965image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:59.417081image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:02.390503image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:05.063622image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:07.469319image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:10.702400image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:13.371759image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:16.018265image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:19.112663image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:21.575184image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:24.050124image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:27.286618image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:31.076869image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:33.742899image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:54.330635image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:57.033270image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:15:59.570422image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:02.548525image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:05.217913image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:07.616734image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:10.999176image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:13.517482image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:16.156328image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:19.279257image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:21.734214image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:24.398897image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:27.449359image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2024-09-14T10:16:31.285220image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2024-09-14T10:16:42.345262image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
avg_flow_rateavg_water_levelavg_water_temperaturemax_flow_ratemax_water_levelmax_water_temperaturemin_flow_ratemin_water_levelmin_water_temperaturemonthsuspended_sediment_erosionsuspended_sediment_transportsuspended_solid_loadsuspended_solids_concentrationyear
avg_flow_rate1.0000.9260.2830.9840.9260.2860.9760.8820.280-0.0010.7400.7520.7520.4160.005
avg_water_level0.9261.0000.3030.8980.9810.3090.9200.9770.2950.0100.6550.6660.6660.3430.173
avg_water_temperature0.2830.3031.0000.2940.3180.9970.2670.2810.9970.3500.2230.2270.2270.1090.090
max_flow_rate0.9840.8980.2941.0000.9310.2950.9370.8340.2930.0020.7440.7550.7550.429-0.036
max_water_level0.9260.9810.3180.9311.0000.3220.8950.9340.3130.0150.6710.6810.6810.3630.113
max_water_temperature0.2860.3090.9970.2950.3221.0000.2720.2910.9890.3390.2260.2310.2310.1120.140
min_flow_rate0.9760.9200.2670.9370.8950.2721.0000.9210.261-0.0050.7090.7220.7220.3860.054
min_water_level0.8820.9770.2810.8340.9340.2910.9211.0000.2690.0050.6140.6240.6240.3090.239
min_water_temperature0.2800.2950.9970.2930.3130.9890.2610.2691.0000.3600.2190.2240.2240.1070.037
month-0.0010.0100.3500.0020.0150.339-0.0050.0050.3601.000-0.041-0.040-0.040-0.066-0.012
suspended_sediment_erosion0.7400.6550.2230.7440.6710.2260.7090.6140.219-0.0411.0000.9730.9730.860-0.024
suspended_sediment_transport0.7520.6660.2270.7550.6810.2310.7220.6240.224-0.0400.9731.0001.0000.880-0.022
suspended_solid_load0.7520.6660.2270.7550.6810.2310.7220.6240.224-0.0400.9731.0001.0000.880-0.022
suspended_solids_concentration0.4160.3430.1090.4290.3630.1120.3860.3090.107-0.0660.8600.8800.8801.000-0.056
year0.0050.1730.090-0.0360.1130.1400.0540.2390.037-0.012-0.024-0.022-0.022-0.0561.000

Missing values

2024-09-14T10:16:34.001391image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-14T10:16:34.367556image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-14T10:16:34.816153image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

dateavg_water_temperaturemax_water_temperaturemin_water_temperatureavg_water_levelmax_water_levelmin_water_levelsuspended_solids_concentrationsuspended_solid_loadsuspended_sediment_erosionsuspended_sediment_transportavg_flow_ratemax_flow_ratemin_flow_rateyearmonth
01980-11-018.18.18.17186595.5725.560.010.3047.963.437.0198011
11980-11-027.37.37.35962545.1021.540.010.2536.739.632.7198011
21980-11-035.85.85.86883485.1420.160.010.2345.860.027.9198011
31980-11-045.65.65.67780737.6031.260.010.3653.456.949.9198011
41980-11-054.84.84.87789709.0337.830.010.4454.166.547.0198011
51980-11-064.74.74.77582645.5922.190.010.2651.458.841.3198011
61980-11-076.16.16.16272444.5816.040.010.1939.448.824.8198011
71980-11-087.77.77.756654311.3440.000.010.4634.542.224.0198011
81980-11-097.57.57.555644313.5342.450.020.4933.341.224.0198011
91980-11-107.67.67.648604015.2942.920.020.5027.837.621.8198011
dateavg_water_temperaturemax_water_temperaturemin_water_temperatureavg_water_levelmax_water_levelmin_water_levelsuspended_solids_concentrationsuspended_solid_loadsuspended_sediment_erosionsuspended_sediment_transportavg_flow_ratemax_flow_ratemin_flow_rateyearmonth
152782024-08-2217.618.416.713113612534.95NaNNaNNaN68.075.258.820248
152792024-08-2318.019.116.912713012134.95NaNNaNNaN61.866.253.020248
152802024-08-2418.820.017.612613112034.95NaNNaNNaN60.567.751.620248
152812024-08-2518.319.717.512114411134.95NaNNaNNaN52.787.539.720248
152822024-08-2616.417.416.114215013134.95NaNNaNNaN84.296.967.720248
152832024-08-2716.617.615.813013712134.95NaNNaNNaN66.076.753.020248
152842024-08-2817.819.016.611712111334.95NaNNaNNaN47.553.042.220248
152852024-08-2918.719.617.711411611034.95NaNNaNNaN43.246.138.520248
152862024-08-3019.119.818.111411610834.95NaNNaNNaN43.546.136.320248
152872024-08-3119.119.718.210911510434.95NaNNaNNaN37.744.832.020248